Benchmarking foundation potentials against quantum chemistry methods for predicting molecular redox potentials

TL;DR

This paper evaluates machine learning foundation potentials for predicting molecular redox potentials, demonstrating high accuracy for PCET reactions and proposing a hybrid workflow with DFT refinement to improve predictions and screening efficiency.

Contribution

It benchmarks FPs against DFT for redox potentials and introduces a hybrid workflow combining FPs and DFT for scalable virtual screening.

Findings

FPs achieve high accuracy for PCET reactions.

Performance drops for multi-electron ET reactions.

Hybrid workflow improves prediction robustness.

Abstract

Computational high-throughput virtual screening is essential for identifying redox-active molecules for sustainable applications such as electrochemical carbon capture. A primary challenge in this approach is the high computational cost associated with accurate quantum chemistry calculations. Machine learning foundation potentials (FPs) trained on extensive density functional theory (DFT) calculations offer a computationally efficient alternative. Here, we benchmark the MACE-OMol-0 and UMA FPs against a hierarchy of DFT functionals for predicting experimental molecular redox potentials for both electron transfer (ET) and proton-coupled electron transfer (PCET) reactions. We find that these FPs achieve exceptional accuracy for PCET processes, rivaling their target DFT method. However, the performance is diminished for ET reactions, particularly for multi-electron transfers involving reactive ions that are underrepresented in the OMol25 training data, revealing a key out-of-distribution limitation. To overcome this, we propose an optimal hybrid workflow that uses the FPs for efficient geometry optimization and thermochemical analysis, followed by a crucial single-point DFT energy refinement and an implicit solvation correction. This pragmatic approach provides a robust and scalable strategy for accelerating high-throughput virtual screening in sustainable chemistry.